A bootstrapping approach to annotating large image collection

Huge amount of manual efforts are required to annotate large image/video archives with text annotations. Several recent works attempted to automate this task by employing supervised learning approaches to associate visual information extracted in segmented images with semantic concepts provided by associated text. The main limitation of such approaches, however, is that large labeled training corpus is still needed for effective learning, and semantically meaningful segmentation for images is in general unavailable. This paper explores the use of bootstrapping approach to tackle this problem. The idea is to start from a small set of labeled training examples, and successively annotate a larger set of unlabeled examples. This is done using the cotraining approach, in which two “statistically independent” classifiers are used to co-train and co-annotate the unlabeled examples. An active learning approach is used to select the best examples to label at each stage of learning in order...

• Type: Conference
• Year: 2003
• Where: MIR
• Authors: HuaMin Feng, Tat-Seng Chua
• Keywords: Bootstrapping Approach, Learning Approach, MIR 2003, Multimedia, Unlabeled Examples